UMD faculty and students will partner with Capital One research scientists in 2018 to study machine learning, data analytics and cybersecurity using diverse perspectives, technologies and applications. This new FIRE stream will allow students who have interest in these fields to start tackling critical challenges in these important areas of research.
About Machine LearningMachine learning is a subdiscipline of computer science focused on the capacities for computer systems to learn without being purposefully programmed to perform specific tasks. These capacities derive from pattern recognition and artificial intelligence (AI). The Capital One Machine Learning research stream will focus on using machine learning to develop algorithms involved in predictive data analytics using both supervised and unsupervised approaches.
Potential Research Question - Fraudulent Activity DetectionDetection of fraudulent activity in commercial transactions presents a significant opportunity potentially worth billions of dollars per year. Creating automated systems that can detect fraud is a potential natural machine learning problem. Given historical transaction data, can we design an algorithm that learns to detect fraudulent transactions?
Such a system could use historical transaction data in a variety of ways:
First-Year Spring Semester Course:
FIRE171 - FIRE COURSE 2: Capital One Machine Learning
(3 credits, General Education Distributive Studies, Natural Sciences)
Second-Year Fall Semester Course:
FIRE271 - FIRE COURSE 3: Capital One Machine Learning
(3 credits, General Education Scholarship in Practice)